INFO-H-500 Image Acquisition & Processing
  • Home
  • 1.Introduction
    • Biological vision
    • Image sensors
    • Image representation
  • 2.Low-level image processing
    • Histogram
    • Linear filtering
    • Rank based filters
    • Image restoration
    • Edge detection
  • 3.Image segmentation
    • Typical image processing pipelines
    • Histogram based segmentation
    • Border based segmentation
    • Region based segmentation
    • Model based segmentation
      • Model based segmentation
      • Active contours
      • Hough transform
    • Examples
  • 4.Morphomathematics
    • Morphomathematical operators
    • Combined operations
    • The watershed transform
    • Gray level morphology
  • 5.Objects features
    • Statistical features
    • Contour features
    • Object moments
    • Texture features
  • LABS
  • References
  • About
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In [1]:
%matplotlib inline
from IPython.display import HTML,Image,SVG,YouTubeVideo
In [2]:
from skimage import data
import numpy as np
from skimage.morphology import disk
import skimage.filters.rank as skr
from skimage.measure import label
from skimage.morphology import watershed
from skimage.io import imread
from scipy import ndimage as ndi
import matplotlib.pyplot as plt
from skimage.segmentation import mark_boundaries
In [3]:
# segment the coins
im = data.coins()
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
In [4]:
# detect the eyes / nose
im = data.chelsea()
plt.imshow(im);
In [5]:
# counting the galaxies
im = data.hubble_deep_field()
plt.imshow(im);
In [6]:
im = data.page()

bg = skr.median(im, disk(10))

res = (1.*im/bg) < .8

plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
plt.figure()
plt.imshow(bg,cmap=plt.cm.gray);
plt.colorbar()
plt.figure()
plt.imshow(res.astype(np.uint8),cmap=plt.cm.gray);
plt.colorbar();
In [7]:
# segment the cells
im = imread('../data/dh_phase.png')
th = im>150
th1 = im>100

plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
plt.figure()
plt.imshow(1.*th+th1,cmap=plt.cm.gray)
plt.colorbar();
In [8]:
from skimage.feature import canny


ca = canny(im)

plt.figure(figsize=[10,10])
plt.imshow(ca,cmap=plt.cm.gray);
In [9]:
from skimage.morphology import watershed
from skimage.segmentation import mark_boundaries
lab,n_lab = label(th,return_num=True)
bg = th1==0
lab[bg] = n_lab+1

#med = skr.median(im,disk(5))
#gr = skr.gradient(med,disk(3))

ws = watershed(255-im,lab)
plt.imshow(mark_boundaries(im,ws))
/home/olivier/.conda/envs/py3/lib/python3.7/site-packages/skimage/morphology/_deprecated.py:5: skimage_deprecation: Function ``watershed`` is deprecated and will be removed in version 0.19. Use ``skimage.segmentation.watershed`` instead.
  def watershed(image, markers=None, connectivity=1, offset=None, mask=None,
Out[9]:
<matplotlib.image.AxesImage at 0x7f523271ba90>
In [10]:
im = imread('../data/exp0001.jpg')
plt.figure(figsize=[20,20])
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
In [11]:
# count red and yellow flowers
im = imread('../data/flowers.jpg')
plt.imshow(im)
plt.colorbar();
In [12]:
# find the fiber orientation
im = imread('../data/image4.png')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
In [13]:
from IPython.display import YouTubeVideo
YouTubeVideo('PUcz11MLxUk', start=0, autoplay=1, theme="light", color="blue",)
Out[13]:
In [14]:
# detect stroma
im = imread('../data/Rp042826d.jpg')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
In [15]:
# segment the flowers
im = imread('../data/KaneFlowers.jpg')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
In [16]:
from skimage.morphology import watershed
from skimage.segmentation import mark_boundaries

gr = skr.gradient(im,disk(3))

local_min = im <= skr.minimum(im,disk(5))

lab = label(local_min)

#med = skr.median(im,disk(5))

ws = watershed(gr,lab)

plt.figure(figsize=[10,10])
plt.imshow(mark_boundaries(im,ws))

#plt.imshow(local_min)
/home/olivier/.conda/envs/py3/lib/python3.7/site-packages/skimage/morphology/_deprecated.py:5: skimage_deprecation: Function ``watershed`` is deprecated and will be removed in version 0.19. Use ``skimage.segmentation.watershed`` instead.
  def watershed(image, markers=None, connectivity=1, offset=None, mask=None,
Out[16]:
<matplotlib.image.AxesImage at 0x7f5231b39b10>
In [17]:
rgb = imread('../data/4colors.JPG')

plt.figure(figsize=[20,20])
plt.imshow(rgb)
plt.colorbar();
In [18]:
r = skr.median(rgb[:,:,0],disk(1))
plt.imshow(r,cmap=plt.cm.gray)
Out[18]:
<matplotlib.image.AxesImage at 0x7f52319056d0>
In [19]:
s = rgb.sum(axis=2)
th = s > 100

#post-processing
pth = skr.minimum(th.astype(np.uint8),disk(1))

plt.figure(figsize=[20,20])
plt.imshow(pth,cmap=plt.cm.gray)
plt.colorbar()
Out[19]:
<matplotlib.colorbar.Colorbar at 0x7f5231b396d0>
In [20]:
lab = label(pth)

lut = np.arange(0,np.max(lab)+1)

plt.imshow(lab)
plt.colorbar()

mask = lab == 20
plt.imshow(mask)
Out[20]:
<matplotlib.image.AxesImage at 0x7f523248e450>
In [21]:
from random import shuffle
shuffle(lut)
In [22]:
shuffle(lut)
plt.imshow(lut[lab])
plt.colorbar()
Out[22]:
<matplotlib.colorbar.Colorbar at 0x7f523402e210>
In [ ]:

In [23]:
# segment the cell
im = imread('../data/exp0001crop.jpg')
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
In [24]:
m = skr.median(im,disk(5))
plt.imshow(m,cmap=plt.cm.gray)
plt.colorbar()
Out[24]:
<matplotlib.colorbar.Colorbar at 0x7f523248ff90>
In [25]:
th1 = m < 90

th2 = np.bitwise_and(110 > m,m < 130)

plt.imshow(th2)
Out[25]:
<matplotlib.image.AxesImage at 0x7f52324c8090>
In [26]:
markers = label(th2)
plt.imshow(markers)
plt.colorbar()
Out[26]:
<matplotlib.colorbar.Colorbar at 0x7f5231a73a90>
In [27]:
markers[markers==3] = 2
ws = watershed(im,markers)
/home/olivier/.conda/envs/py3/lib/python3.7/site-packages/skimage/morphology/_deprecated.py:5: skimage_deprecation: Function ``watershed`` is deprecated and will be removed in version 0.19. Use ``skimage.segmentation.watershed`` instead.
  def watershed(image, markers=None, connectivity=1, offset=None, mask=None,
In [28]:
plt.imshow(ws)
plt.imshow(mark_boundaries(im,ws))
Out[28]:
<matplotlib.image.AxesImage at 0x7f52325ca8d0>
In [29]:
# segment the cell
im = imread('../data/brain.jpg')[:,:,0]
plt.figure(figsize=(10,10))
plt.imshow(im,cmap=plt.cm.gray)
plt.colorbar();
In [30]:
plt.hist(im.flatten(),255);
In [31]:
from skimage.filters import threshold_otsu

t_otsu = threshold_otsu(im)
t_otsu
Out[31]:
36
In [32]:
th = im > t_otsu
plt.figure(figsize=(10,10))
plt.imshow(th)
Out[32]:
<matplotlib.image.AxesImage at 0x7f5234073190>
In [33]:
lab = label(th,connectivity=1)
plt.imshow(lab)
Out[33]:
<matplotlib.image.AxesImage at 0x7f5231654b50>
In [34]:
from skimage.measure import regionprops
In [35]:
props = regionprops(lab)

brain = (lab==7).astype(np.uint8)

pp = skr.maximum(brain,disk(3))
pp = skr.minimum(pp,disk(3))

plt.imshow(pp)
Out[35]:
<matplotlib.image.AxesImage at 0x7f52315da090>
In [36]:
for p in props:
    print(p.area, p.label)
1459 1
5 2
1 3
3 4
1 5
16 6
6323 7
1 8
2 9
1 10
1 11
1 12
1 13
16 14
1 15
1 16
1 17
2 18
2 19
2 20
2 21
30 22
1 23
1 24
1 25
1 26
2 27
2 28
5 29
1 30
1 31
2 32
1 33
13 34
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